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Dryad

Dataset for: Aboveground carbon stocks, woody and litter productivity along an elevational gradient in the Rwenzori Mountains, Uganda

Cite this dataset

Okello, Joseph et al. (2022). Dataset for: Aboveground carbon stocks, woody and litter productivity along an elevational gradient in the Rwenzori Mountains, Uganda [Dataset]. Dryad. https://doi.org/10.5061/dryad.4mw6m90cr

Abstract

Montane forests are characterized by high biodiversity, endemism and strong elevational environmental gradients. The latter attribute makes them also suitable as a ‘natural laboratory’ for studying the effects of environmental parameters on ecosystem functions. To provide better insight into the carbon cycle of Afromontane ecosystems, we used an elevational gradient approach to quantify carbon stocks, woody and litter productivity, and their constraining factors. Twenty plots were established, covering five elevations from Kibale Forest at 1250 m to 3000 m in the Rwenzori Mountains. Results revealed aboveground carbon stocks of between 185.4 ± 48.9 Mg C ha-1 and 70.8 ±18.6 Mg C ha-1 at 1250-1300 m and 2700-3000 m respectively. Aboveground carbon tended to decrease with elevation, but this trend was not significant. This was due to similarities in stem diameter combined with different effects of tree height and stem density. Similarly, woody productivity did not change with elevation, ranging from 8.3 ± 4.1 Mg C ha-1 year-1 to 3.4 ± 1.5 Mg C ha-1 year-1 at 2500-2600 m and 2700-3000 m respectively. However, litter productivity decreased linearly by 0.14 ± 0.04 Mg C ha-1 year-1 per 100 m of elevation increase, ranging from 4.0 ± 0.7 Mg C ha-1 year-1 at 1750-1850 m to 1.2 Mg C ha-1 year-1 at 2700-3000 m. Topsoil physicochemical properties varied with elevation, but showed no significant relationship with carbon stocks and woody productivity. However, litter productivity increased with mean soil temperature, whereas it decreased with soil total nitrogen.

Methods

Raw data collected from the field. Plot level average values were then computed.

Funding

Vlaamse Interuniversitaire Raad, Award: UG2019IUC027A103